






"""simple docstring"""





import json
from typing import TYPE_CHECKING, List, Optional, Tuple

from tokenizers import pre_tokenizers

from ...tokenization_utils_base import BatchEncoding
from ...tokenization_utils_fast import PreTrainedTokenizerFast
from ...utils import logging
from .tokenization_gpta import GPTaTokenizer


if TYPE_CHECKING:
  from transformers.pipelines.conversational import Conversation


_SCREAMING_SNAKE_CASE   :							Dict          =		logging.get_logger(__name__)

_SCREAMING_SNAKE_CASE   :							List[Any]          =		{'''vocab_file''': '''vocab.json''', '''merges_file''': '''merges.txt''', '''tokenizer_file''': '''tokenizer.json'''}

_SCREAMING_SNAKE_CASE   :							str          =		{
    '''vocab_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/vocab.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/vocab.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/vocab.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/vocab.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/vocab.json''',
    },
    '''merges_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/merges.txt''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/merges.txt''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/merges.txt''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/merges.txt''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/merges.txt''',
    },
    '''tokenizer_file''': {
        '''gpt2''': '''https://huggingface.co/gpt2/resolve/main/tokenizer.json''',
        '''gpt2-medium''': '''https://huggingface.co/gpt2-medium/resolve/main/tokenizer.json''',
        '''gpt2-large''': '''https://huggingface.co/gpt2-large/resolve/main/tokenizer.json''',
        '''gpt2-xl''': '''https://huggingface.co/gpt2-xl/resolve/main/tokenizer.json''',
        '''distilgpt2''': '''https://huggingface.co/distilgpt2/resolve/main/tokenizer.json''',
    },
}

_SCREAMING_SNAKE_CASE   :							Optional[Any]          =		{
    '''gpt2''': 1024,
    '''gpt2-medium''': 1024,
    '''gpt2-large''': 1024,
    '''gpt2-xl''': 1024,
    '''distilgpt2''': 1024,
}






class 	a		(   __snake_case						):
    SCREAMING_SNAKE_CASE		:	Tuple		=					VOCAB_FILES_NAMES
    SCREAMING_SNAKE_CASE		:	Dict		=					PRETRAINED_VOCAB_FILES_MAP
    SCREAMING_SNAKE_CASE		:	str		=					PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    SCREAMING_SNAKE_CASE		:	Union[str, Any]		=					["""input_ids""", """attention_mask"""]
    SCREAMING_SNAKE_CASE		:	Optional[Any]		=					GPTaTokenizer






    def __init__(			self	:				Optional[int]			,		__SCREAMING_SNAKE_CASE	:				str=None			,		__SCREAMING_SNAKE_CASE	:				Tuple=None			,		__SCREAMING_SNAKE_CASE	:				Tuple=None			,		__SCREAMING_SNAKE_CASE	:				str="<|endoftext|>"			,		__SCREAMING_SNAKE_CASE	:				List[Any]="<|endoftext|>"			,		__SCREAMING_SNAKE_CASE	:				Union[str, Any]="<|endoftext|>"			,		__SCREAMING_SNAKE_CASE	:				List[Any]=False			,		**__SCREAMING_SNAKE_CASE	:				Dict			,		)	->						Tuple:
      super().__init__(
          __SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		tokenizer_file=__SCREAMING_SNAKE_CASE			,		unk_token=__SCREAMING_SNAKE_CASE			,		bos_token=__SCREAMING_SNAKE_CASE			,		eos_token=__SCREAMING_SNAKE_CASE			,		add_prefix_space=__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE			,		)

      lowerCamelCase_       	=		kwargs.pop('add_bos_token'			,		__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		json.loads(self.backend_tokenizer.pre_tokenizer.__getstate__()   )
      if pre_tok_state.get('add_prefix_space'			,		__SCREAMING_SNAKE_CASE   ) != add_prefix_space:
        lowerCamelCase_       	=		getattr(__SCREAMING_SNAKE_CASE			,		pre_tok_state.pop('type'   )   )
        lowerCamelCase_       	=		add_prefix_space
        lowerCamelCase_       	=		pre_tok_class(**__SCREAMING_SNAKE_CASE   )

      lowerCamelCase_       	=		add_prefix_space






    def   UpperCamelCase			(			self	:				Optional[Any]			,		*__SCREAMING_SNAKE_CASE	:				Any			,		**__SCREAMING_SNAKE_CASE	:				Any   )	->						BatchEncoding:
      lowerCamelCase_       	=		kwargs.get('is_split_into_words'			,		__SCREAMING_SNAKE_CASE   )
      assert self.add_prefix_space or not is_split_into_words, (
          F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._batch_encode_plus(*__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				Dict			,		*__SCREAMING_SNAKE_CASE	:				Tuple			,		**__SCREAMING_SNAKE_CASE	:				List[Any]   )	->						BatchEncoding:
      lowerCamelCase_       	=		kwargs.get('is_split_into_words'			,		__SCREAMING_SNAKE_CASE   )

      assert self.add_prefix_space or not is_split_into_words, (
          F'''You need to instantiate {self.__class__.__name__} with add_prefix_space=True '''
          "to use it with pretokenized inputs."
      )

      return super()._encode_plus(*__SCREAMING_SNAKE_CASE			,		**__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				str			,		__SCREAMING_SNAKE_CASE	:				Optional[str] = None   )	->						Tuple[str]:
      lowerCamelCase_       	=		self._tokenizer.model.save(__SCREAMING_SNAKE_CASE			,		name=__SCREAMING_SNAKE_CASE   )
      return tuple(__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[str]			,		__SCREAMING_SNAKE_CASE	:				"Conversation"   )	->						List[int]:
      lowerCamelCase_       	=		[]
      for is_user, text in conversation.iter_texts():
        input_ids.extend(self.encode(__SCREAMING_SNAKE_CASE			,		add_special_tokens=__SCREAMING_SNAKE_CASE   ) + [self.eos_token_id]   )

      if len(__SCREAMING_SNAKE_CASE   ) > self.model_max_length:
        lowerCamelCase_       	=		input_ids[-self.model_max_length :]
      return input_ids












"""simple docstring"""





import unittest

from transformers import is_tf_available
from transformers.testing_utils import require_tf


if is_tf_available():
  import tensorflow as tf
  from tensorflow.python.eager import context
  from tensorflow.python.framework import ops

  from transformers import GradientAccumulator, create_optimizer






@require_tf
class 	a		(   unittest.TestCase						):






    def   UpperCamelCase			(			self	:				int			,		__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Dict   )	->						str:
      self.assertEqual(len(__SCREAMING_SNAKE_CASE   )			,		len(__SCREAMING_SNAKE_CASE   )   )
      for a, b in zip(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE   ):
        self.assertAlmostEqual(__SCREAMING_SNAKE_CASE			,		__SCREAMING_SNAKE_CASE			,		delta=__SCREAMING_SNAKE_CASE   )






    def   UpperCamelCase			(			self	:				List[Any]   )	->						int:
      lowerCamelCase_       	=		GradientAccumulator()
      accumulator([tf.constant([1.0, 2.0]   )]   )
      accumulator([tf.constant([-2.0, 1.0]   )]   )
      accumulator([tf.constant([-1.0, 2.0]   )]   )
      with self.assertRaises(__SCREAMING_SNAKE_CASE   ):
        accumulator([tf.constant([1.0, 1.0]   ), tf.constant([2.0, 2.0]   )]   )
      self.assertEqual(accumulator.step			,		3   )
      self.assertEqual(len(accumulator.gradients   )			,		1   )
      self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()			,		[-2.0, 5.0]			,		tol=1e-2   )
      accumulator.reset()
      self.assertEqual(accumulator.step			,		0   )
      self.assertListAlmostEqual(accumulator.gradients[0].numpy().tolist()			,		[0.0, 0.0]			,		tol=1e-2   )






    def   UpperCamelCase			(			self	:				str   )	->						Any:
      lowerCamelCase_       	=		None
      ops.enable_eager_execution_internal()
      lowerCamelCase_       	=		tf.config.list_physical_devices('CPU'   )
      if len(__SCREAMING_SNAKE_CASE   ) == 1:
        tf.config.set_logical_device_configuration(
            physical_devices[0]			,		[tf.config.LogicalDeviceConfiguration(), tf.config.LogicalDeviceConfiguration()]   )
      lowerCamelCase_       	=		tf.config.list_logical_devices(device_type='CPU'   )
      lowerCamelCase_       	=		tf.distribute.MirroredStrategy(devices=devices[:2]   )

      with strategy.scope():
        lowerCamelCase_       	=		GradientAccumulator()
        lowerCamelCase_       	=		tf.Variable([4.0, 3.0]   )
        lowerCamelCase_	,      lowerCamelCase_       	=		create_optimizer(5e-5			,		10			,		5   )
        lowerCamelCase_       	=		tf.Variable([0.0, 0.0]			,		trainable=__SCREAMING_SNAKE_CASE   )

      def accumulate_on_replica(__SCREAMING_SNAKE_CASE	:				Union[str, Any]   ):
        accumulator([gradient]   )

      def apply_on_replica():
        optimizer.apply_gradients(list(zip(accumulator.gradients			,		[variable]   )   )   )

      @tf.function
      def accumulate(__SCREAMING_SNAKE_CASE	:				Any			,		__SCREAMING_SNAKE_CASE	:				Any   ):
        with strategy.scope():
          lowerCamelCase_       	=		strategy.experimental_local_results(__SCREAMING_SNAKE_CASE   )
          local_variables[0].assign(__SCREAMING_SNAKE_CASE   )
          local_variables[1].assign(__SCREAMING_SNAKE_CASE   )
          strategy.run(__SCREAMING_SNAKE_CASE			,		args=(gradient_placeholder,)   )

      @tf.function
      def apply_grad():
        with strategy.scope():
          strategy.run(__SCREAMING_SNAKE_CASE   )

      def _check_local_values(__SCREAMING_SNAKE_CASE	:				int			,		__SCREAMING_SNAKE_CASE	:				List[str]   ):
        lowerCamelCase_       	=		strategy.experimental_local_results(accumulator._gradients[0]   )
        self.assertListAlmostEqual(values[0].value()			,		__SCREAMING_SNAKE_CASE			,		tol=1e-2   )
        self.assertListAlmostEqual(values[1].value()			,		__SCREAMING_SNAKE_CASE			,		tol=1e-2   )

      accumulate([1.0, 2.0]			,		[-1.0, 1.0]   )
      accumulate([3.0, -1.0]			,		[-1.0, -1.0]   )
      accumulate([-2.0, 2.0]			,		[3.0, -2.0]   )
      self.assertEqual(accumulator.step			,		3   )
      _check_local_values([2.0, 3.0]			,		[1.0, -2.0]   )
      apply_grad()
      self.assertListAlmostEqual(variable.value()			,		[4.0, 3.0]			,		tol=1e-2   )
      accumulator.reset()
      self.assertEqual(accumulator.step			,		0   )
      _check_local_values([0.0, 0.0]			,		[0.0, 0.0]   )




